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Developing Open Source Educational Resources for Machine Learning and Data Science

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Pitfalls to Avoid when Interpreting Machine Learning Models., , , , , , , , and . CoRR, (2020)Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results., , , , and . WIREs Data Mining Knowl. Discov., (2022)On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models.. Ludwig Maximilian University of Munich, Germany, (2019)Interpretable Regional Descriptors: Hyperbox-Based Local Explanations., , , and . ECML/PKDD (3), volume 14171 of Lecture Notes in Computer Science, page 479-495. Springer, (2023)Developing Open Source Educational Resources for Machine Learning and Data Science., , , , , , and . Teaching ML, volume 207 of Proceedings of Machine Learning Research, page 1-6. PMLR, (2022)Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution, , , and . (2022)mlr3: A modern object-oriented machine learning framework in R., , , , , , , , , and . J. Open Source Softw., 4 (44): 1903 (2019)Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition., , and . CoRR, (2019)General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models., , , , , , , , and . xxAI@ICML, volume 13200 of Lecture Notes in Computer Science, page 39-68. Springer, (2020)fmeffects: An R Package for Forward Marginal Effects., , , , and . CoRR, (2023)